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    Editorial "Joint modeling of longitudinal and time-to-event data and beyond"
    Suarez, CC ; Klein, N ; Kneib, T ; Molenberghs, G ; Rizopoulos, D (WILEY, 2017-11-01)
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    Studying the relationship between a woman's reproductive lifespan and age at menarche using a Bayesian multivariate structured additive distributional regression model
    Duarte, E ; de Sousa, B ; Cadarso-Suarez, C ; Klein, N ; Kneib, T ; Rodrigues, V (WILEY, 2017-11-01)
    Studies addressing breast cancer risk factors have been looking at trends relative to age at menarche and menopause. These studies point to a downward trend of age at menarche and an upward trend for age at menopause, meaning an increase of a woman's reproductive lifespan cycle. In addition to studying the effect of the year of birth on the expectation of age at menarche and a woman's reproductive lifespan, it is important to understand how a woman's cohort affects the correlation between these two variables. Since the behavior of age at menarche and menopause may vary with the geographic location of a woman's residence, the spatial effect of the municipality where a woman resides needs to be considered. Thus, a Bayesian multivariate structured additive distributional regression model is proposed in order to analyze how a woman's municipality and year of birth affects a woman's age of menarche, her lifespan cycle, and the correlation of the two. The data consists of 212,517 postmenopausal women, born between 1920 and 1965, who attended the breast cancer screening program in the central region of Portugal.
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    Boosting joint models for longitudinal and time-to-event data
    Waldmann, E ; Taylor-Robinson, D ; Klein, N ; Kneib, T ; Pressler, T ; Schmid, M ; Mayr, A (WILEY, 2017-11-01)
    Joint models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent the potential bias introduced by independent modeling. Commonly, joint models are estimated in likelihood-based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and that do not immediately work for high-dimensional data. In this paper, we propose a boosting algorithm tackling these challenges by being able to simultaneously estimate predictors for joint models and automatically select the most influential variables even in high-dimensional data situations. We analyze the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry that collects longitudinal lung function data on patients with cystic fibrosis together with data regarding the onset of pulmonary infections. This is the first approach to combine state-of-the art algorithms from the field of machine-learning with the model class of joint models, providing a fully data-driven mechanism to select variables and predictor effects in a unified framework of boosting joint models.
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    Mixed binary-continuous copula regression models with application to adverse birth outcomes
    Klein, N ; Kneib, T ; Marra, G ; Radice, R ; Rokicki, S ; McGovern, ME (Wiley, 2019-02-10)
    Bivariate copula regression allows for the flexible combination of two arbitrary, continuous marginal distributions with regression effects being placed on potentially all parameters of the resulting bivariate joint response distribution. Motivated by the risk factors for adverse birth outcomes, many of which are dichotomous, we consider mixed binary‐continuous responses that extend the bivariate continuous framework to the situation where one response variable is discrete (more precisely, binary) whereas the other response remains continuous. Utilizing the latent continuous representation of binary regression models, we implement a penalized likelihood–based approach for the resulting class of copula regression models and employ it in the context of modeling gestational age and the presence/absence of low birth weight. The analysis demonstrates the advantage of the flexible specification of regression impacts including nonlinear effects of continuous covariates and spatial effects. Our results imply that racial and spatial inequalities in the risk factors for infant mortality are even greater than previously suggested.
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    Quality and resource efficiency in hospital service provision: A geoadditive stochastic frontier analysis of stroke quality of care in Germany
    Pross, C ; Strumann, C ; Geissler, A ; Herwartz, H ; Klein, N ; Arrieta, A (PUBLIC LIBRARY SCIENCE, 2018-09-06)
    We specify a Bayesian, geoadditive Stochastic Frontier Analysis (SFA) model to assess hospital performance along the dimensions of resources and quality of stroke care in German hospitals. With 1,100 annual observations and data from 2006 to 2013 and risk-adjusted patient volume as output, we introduce a production function that captures quality, resource inputs, hospital inefficiency determinants and spatial patterns of inefficiencies. With high relevance for hospital management and health system regulators, we identify performance improvement mechanisms by considering marginal effects for the average hospital. Specialization and certification can substantially reduce mortality. Regional and hospital-level concentration can improve quality and resource efficiency. Finally, our results demonstrate a trade-off between quality improvement and resource reduction and substantial regional variation in efficiency.